Business Intelligence generally refers to software tools used to improve business enterprise decision-making. These tools are commonly applied to financial, human resource, marketing, sales, customer, and supplier analyses. More specifically, these tools can include: reporting and analysis tools to present information; content delivery infrastructure systems for delivery and management of reports and analytics; data warehousing systems for cleansing and consolidating information from disparate sources; and data management systems, such as relational databases, On Line Analytic Processing (OLAP) systems, or other data sources used to collect, store, and manage raw data.
In many organizations data is stored in multiple formats that are not readily compatible, such as relational and OLAP data sources. Additionally, in many organizations it is desirable to insulate a user from the complexities of the underlying data source. Therefore, it is advantageous to be able to work with data using a semantic layer that provides terms and abstracted logic associated with the underlying data. Semantic layers for relational databases are known in the art.
Systems designed to provide semantic layer definitions for underlying data within an organization are typically determined by a small group of people based on an understanding of internal data needs and existing data sources, such as relational and OLAP databases. These semantic layers or domains are not designed to be collaboratively defined with any number of business and individual definitions. Semantic layers or domains are not typically designed to be associated with partial or highly fragmented data records from a wide range of data sources and supplied by a widely disparate user base that may apply any number of business and individual definitions to the partial data.
In view of the foregoing, it would be advantageous to enhance the architecture of known semantic layers and domains to support collaborative semantic definitions of data based on a wide range of contributors to both the data set and the semantic definitions associated with the data set. Preferably, these collaborations would include adding complete or partial data records and identifying the relationship between the partial data record and an existing semantic definition, and modifying the semantic definition itself based on collaborative processes.